Identification of Fake Reviews Using Supervised Machine Learning
Keywords:
Machine learning, fake, reviews, Logistic Regression.Abstract
Online reviews are largely regarded as a significant aspect for establishing and preserving a solid reputation as e-commerce systems continue to advance. Additionally, they play a significant part in how end customers decide. A favorable review for a specific item typically draws in more customers and increases sales significantly. In order to develop a virtual reputation and draw in new clients, reviews that are false or misleading are being intentionally written. Therefore, spotting bogus reviews is an active and developing study field. The ability to spot false reviews depends on both the essential characteristics of the reviews and the behaviour of the reviewers. This study suggests using machine learning to spot bogus reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviours of the reviewers. The performance of many experiments conducted on a real dataset of restaurant reviews from Yelp is compared in this research, including KNN, Naive Bayes (NB), and Logistic Regression. The findings show that Logistic Regression performs better than the other classifiers in terms of accuracy. The findings demonstrate that the algorithm is better able to distinguish between authentic and false reviews.
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